Multivariate time series with Prophet Facebook and LSTM algorithm to predict the energy consumption

Abstract

Energy is one of the most important factors in a country growth, both in the industrial and household fields. Among these fields, the industrial sector that needs the most in supporting the development of a company, energy saving is an important target for every company. Therefore, an accurate prediction is needed to determine future energy consumption. Many researchers have proposed research on the prediction of energy consumption using either machine learning or deep learning. One of the challenging factors in predicting energy consumption is data using a multivariate time series model with several uses in the area. In this project, researchers will conduct research on energy consumption predictions in manufacturing companies engaged in the food sector. This company has several areas as well as several predictable energies such as electricity, water, and diesel fuel, the data studied are multivariate time series modeled data. For the case of a data model like this, we use two algorithms, namely prophet and LSTM, because this algorithm can predict time series data. From the results of our research, it shows that Prophet Facebook which has the best results in predicting the energy consumption of electricity, water, and diesel fuel, a very significant difference in error rate is obtained by the LSTM algorithm for predicting time series models.